CN107767185A - A kind of accurate methodology of the electricity charge of sharing base - Google Patents
A kind of accurate methodology of the electricity charge of sharing base Download PDFInfo
- Publication number
- CN107767185A CN107767185A CN201711057721.6A CN201711057721A CN107767185A CN 107767185 A CN107767185 A CN 107767185A CN 201711057721 A CN201711057721 A CN 201711057721A CN 107767185 A CN107767185 A CN 107767185A
- Authority
- CN
- China
- Prior art keywords
- base station
- shared
- operator
- electric
- shared base
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 23
- 230000005611 electricity Effects 0.000 title abstract description 16
- 238000005265 energy consumption Methods 0.000 claims abstract description 26
- 238000012360 testing method Methods 0.000 claims description 19
- 238000012417 linear regression Methods 0.000 claims description 10
- 230000007613 environmental effect Effects 0.000 claims description 7
- 238000005457 optimization Methods 0.000 claims description 6
- 230000001419 dependent effect Effects 0.000 claims description 5
- 239000000463 material Substances 0.000 claims description 4
- 230000008859 change Effects 0.000 claims description 3
- 230000009471 action Effects 0.000 claims description 2
- 239000002245 particle Substances 0.000 claims description 2
- 238000000205 computational method Methods 0.000 abstract 1
- 238000004134 energy conservation Methods 0.000 abstract 1
- 238000011156 evaluation Methods 0.000 abstract 1
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 14
- 230000006872 improvement Effects 0.000 description 7
- 229910052742 iron Inorganic materials 0.000 description 7
- 238000004891 communication Methods 0.000 description 5
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009440 infrastructure construction Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0283—Price estimation or determination
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Accounting & Taxation (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Finance (AREA)
- General Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Molecular Biology (AREA)
- Game Theory and Decision Science (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Entrepreneurship & Innovation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Human Resources & Organizations (AREA)
- Primary Health Care (AREA)
- Tourism & Hospitality (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of accurate methodology of the electricity charge of sharing base, choose electrical equipment and shared electrical equipment difference pocket watch metering electricity that sharing base Nei Ge operators independently use, electrical equipment electricity and the public power environment system energy consumption relation that each operator independently uses are established, obtains each operator's environment in mobile stations energy consumption apportioning amount；The accurate energy consumption apportioning amount of each operator is calculated with reference to each operator's direct current power consumption.This method provides computational methods especially suitable for each operator's electricity charge Computation for apportionment under the conjunction of extensive sharing base background, or sharing base energy conservation project performance evaluation.
Description
Technical Field
The invention relates to the field of energy consumption metering, in particular to an auxiliary computing method which aims at large-scale shared communication base station electric charge computing, is suitable for accurate sharing of electric charges of operators under the background of a shared base station and relates to the fields of network measurement, computing models, parameter optimization and the like.
Background
The Chinese iron tower company comprehensively accepts infrastructure construction and operation and maintenance management of three communication operators in China, realizes sharing of base station machine rooms and power environment system resources (storage batteries, power distribution equipment, switching power supplies and the like), and provides power guarantee services for the three operators in a unified manner, so that the integration benefit of resource sharing is effectively promoted and fully exerted.
At present, iron tower companies provide two electric charge settlement modes for three operators: one is electric charge packet trunk, namely, different station types of packet trunk charges are agreed between an iron tower and an operator in advance, and the operator pays packet trunk electric charge total amount to an iron tower company regularly; and the other is that according to the mode of 'transparent transmission' of the actual electricity consumption cost of the base station, the iron tower company assists each operator to manage the electricity charge payment procedure, each operator bears all the electricity charges, and all the electricity charges of the site are shared among different operators in the shared base station according to the nominal rated power of the equipment or the proportion of the actual electricity consumption recorded by the direct current meter. The two electric charge settlement modes have advantages and disadvantages in actual implementation: the electric charge is wrapped in a dry mode, the settlement of the charges of the two parties is convenient and simple, the operation is easy, the control risk of the electric charge cost is increased, and an operator lacks the power for saving energy and reducing consumption of main energy consumption equipment of the base station and is not beneficial to promoting the energy saving and reducing consumption of the main equipment of the base station; the electricity charge is subjected to transparent transmission, settlement is carried out according to the actual electricity consumption of the communication base station, the cost of the electricity charge can be effectively controlled within a certain range by the method, but in addition to electricity consumption equipment independently used by each operator in the base station, a large number of equipment is shared equipment, and the electricity consumption consumed by each operator is difficult to accurately determine, so that each operator lacks energy-saving and consumption-reducing power, and in addition, as the electricity consumption cannot be accurately determined, government departments cannot evaluate the energy-saving and emission-reducing effects of each operator and an iron tower company, so that each operator and the iron tower company do not have energy-saving and emission-reducing power.
Disclosure of Invention
In order to solve the problems that an existing electric charge sharing method is rough in algorithm, large in error, high in labor cost and incapable of being popularized in a large scale, the invention provides an accurate electric charge sharing method for a shared base station.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
an accurate allocation method for electric charge of a large-scale shared base station is characterized in that based on energy consumption analysis of a power environment system of the base station, electric equipment independently used by each operator in the shared base station and shared electric equipment are selected to respectively measure electric quantity, and direct-current electric quantity P1 in the electric equipment independently used by each operator is obtained i (i =1,2 \8230) (all devices of operators are supplied with direct current), simultaneously determining the relation between the direct current power consumption of the electrical devices independently used by each operator and the power consumption of the shared electrical devices, establishing a shared base station electric charge sharing model, and obtaining the sharing amount P2 of the energy consumption of the electrical devices shared by each operator i (i =1,2 \8230;), the sum of P1 and P2 is the share of the power consumption of each operator, where i represents a different operator.
In a further improvement, the shared base station electric charge sharing model comprises environmental influence factors, and the environmental influence factors comprise geographical position influence factors and climate influence factors; a building impact factor; the geographic position influence factors comprise urban area influence factors and rural area influence factors; the climate influence factor comprises a temperature influence factor and a humidity influence factor; building form factor, building area factor and enclosure material factor.
Further improvement, comprising the steps of:
step S1: selecting part of shared base stations as data acquisition points;
step S2: performing a meter-hanging test on the direct-current electrical equipment independently used by each operator in the selected shared base station, and recording the current value of the direct-current electrical equipment; collecting environmental impact factors of a test base station;
and step S3: establishing a database according to the electric charge detail data of the selected sharing base station and the direct current electric meter data acquired by the meter hanging test; establishing a relation between direct current electric meter data acquired by a meter hanging test, base station environment influence factors and power consumption of electric equipment shared by operators in a shared base station, and establishing a shared base station electric charge sharing model:
y i ＝β 0 +β 1 x i1 +β 2 x i2 +…β p x ip +α 1 u 1 +α 2 u 2 +α 3 u 3 +α 4 u 4 +ε i (1)
wherein, y i Representing total energy consumption value of the shared electrical equipment, i representing sequence number of the shared base station, x i1 ，x i2 ，…，x ip Respectively representing the direct current meter reading, beta, collected by a wall-mounted meter 0 Indicating error compensation, beta 1 、β 2 ，…β p ，α 1 、α 2 、α 3 、α 4 、α 5 Expressing regression equation coefficients, p different operators, u 1 Representing a geographical influence factor u 1 =1 denotes an urban area influence factor, u 1 =2 denotes rural influence factor, u 2 Denotes the temperature-influencing factor u 5 Factor affecting humidity, u 3 Represents a building area influence factor, u 4 Display enclosureBulk material influence factor, u 4 =1 represents that the enclosure is a brick-concrete structure, u 4 =2 represents that the enclosure is a board house structure, and epsilon represents a random error term;
and step S4: simplifying an electric charge sharing model and optimizing parameters of the sharing model;
and (3) carrying out significance test on the regression coefficient, removing independent variables which have no obvious influence on the dependent variable or can be replaced by the action of other influence factors, and finally, each independent variable in the regression equation is a main factor influencing the change of the dependent variable, thereby establishing a simpler regression equation.
Step S5: and determining parameters of the optimal allocation model to obtain the electric charge allocation proportion of the electric equipment shared by each operator.
Further, in the step S4, a multiple linear regression fitting model is established for the total energy consumption value of the shared electrical devices in the shared base station and the measured value of the dc meters of each operator, so as to optimize and simplify parameters, where a fitness function of the parameter optimization is as follows:
wherein Q (theta) is a fitness function value, y i Representing the total energy consumption value of the common electrical equipment; n represents the number of shared base stations; i represents the sequence number of the shared base station; beta is a 0 Denotes the adjustment factor, p denotes different operators, x i1 ，x i2 ，…，x ip Respectively representing the direct current meter readings collected by the hanging meter; β represents the regression equation coefficients.
The further improvement is that the device is provided with a plurality of grooves,
step Sa: selecting part of shared base stations as data acquisition points;
and Sb: performing a meter hanging test on the direct current electrical equipment independently used by each operator in the selected shared base station, and recording the current value of the direct current electrical equipment;
step Sc: establishing a database according to the electric charge detail data of the selected sharing base station and the direct current electric meter data acquired by the meter hanging test; establishing a relation between direct current electric meter data acquired by meter hanging test and power consumption of electrical equipment shared by operators in a shared base station, and establishing a shared base station electric charge sharing model;
step Sd: optimizing parameters of the apportionment model;
step Se: and determining parameters of the optimal allocation model to obtain the electric charge allocation proportion of the electric equipment shared by each operator, and further obtaining the allocation amount of the energy consumption of the electric equipment shared by each operator.
In the step Sc, performing multiple linear regression fitting on the total energy consumption value of the shared electrical equipment in the shared base station and the measured value of the direct current electric meter of each operator to obtain an electric charge sharing model of the shared base station:
wherein Q (theta) is a fitness function value, y i Representing the total energy consumption value of the common electrical equipment; n represents the number of shared base stations; i represents the sequence number of the shared base station; beta is a 0 Denotes the adjustment factor, p denotes different operators, x i1 ，x i2 ，…，x ip Respectively representing the direct current meter readings collected by the hanging meter; β represents the regression equation coefficient.
In a further improvement, when Q (theta) is at a minimum value, beta 0 ，β 1 β 2 …β p The value of (d) is the optimum value.
Further improvement, for shared electrical equipment in the shared base station, the sharing proportion of the shared operator is as follows: beta is a 1 x i1 ：β 2 x i2 ：…：β p x ip 。
In a further improvement, the number of operators sharing common electrical equipment in the shared base station is two or three.
And further improving, optimizing parameters of the apportionment model by using a quantum-behaved particle swarm algorithm.
Compared with the existing electric charge sharing method, the sharing base station electric charge sharing model provides a modeling method for optimizing parameters of a multiple linear regression model by using QPSO (quench-Polish-Suo) on the basis of fully analyzing static parameters and a large amount of energy consumption sample data of a base station, so that the electric charge sharing accuracy of the sharing base station is effectively improved, the labor cost is greatly reduced, the errors of manual analysis are reduced, and communication operators are promoted to further promote the energy-saving management work of the base station.
Drawings
FIG. 1 is a QPSO-based shared base station electric charge sharing modeling flow chart
FIG. 2 is a schematic diagram of a step structure of an optimized apportionment model;
FIG. 3 is a scatter plot of current measurements in a mobile telecommunications urban area;
FIG. 4 is a fitting effect graph based on a QPSO electric charge sharing model;
FIG. 5 is a QPSO based relative error map.
Detailed Description
The invention is further described in the following with reference to the drawings, but not to limit the scope of protection of the invention.
The sharing base station electric charge sharing method provided by the invention is used for solving the electric charge sharing problem of a large-scale sharing base station. The sharing base station public part energy consumption sharing and the direct current electric quantity value of each communication operator in the base station have a certain positive correlation, and the electric charge sharing proportion of the operators is obtained through the model coefficient by establishing a base station electric charge sharing model.
Example (b):
the QPSO-based shared base station electric charge allocation modeling flow chart is shown in the attached figure 1, and the specific implementation steps are as follows:
step1: and establishing a multiple linear regression model. And establishing a multiple linear regression model by taking the total electric charge as a dependent variable (an explained variable) and the current estimation value of each operator as an independent variable (an explained variable).
Step2：As shown in fig. 2, the multiple linear regression model is optimized.And (3) finding the global minimum value of the function Q (theta) for the fitness function through a QPSO optimization algorithm to obtain a regression model parameter. In a certain area in Hunan, a mobile telecommunication shared base station is taken as an object, and the current value of the base station of the test object is shown in an attached figure 3. It can be seen from fig. 3 that the dc metering values of the two operators and the total amount of the electric charges have a relatively obvious linear relationship. And (4) performing regression model on the mobile telecommunication shared base station to perform significance test analysis of the equation.
The relevant parameters in table 1 were obtained by calculation.
TABLE 1 equation significance test parameters
Given α =0.01, in this model, k =2,n =60, F _0.01 (2,60) =4.98 is found, and F =442.6062> >4.98, the overall linear relationship of the linear model is remarkably true at the level of 0.99. FIGS. 4 and 5 show the optimization results of QPSO optimized multiple linear regression model parameters, and the analysis optimization results: the fitting degree of the electric charge sharing model after the QPSO optimizes the parameters of the multiple linear regression model to the total electric charge is high, and the relative error is smaller.
Step3: and calculating the share base station electric charge sharing proportion.
Obtaining more accurate parameters of the apportionment model from Step 2: beta is a 0 ，β 1 ...，β p And further obtaining the sharing base station electric charge sharing proportion: beta is a 1 x i1 ：β 2 x i2 ：…：β p x ip 。
In summary, the allocation method is based on the establishment of the collection of a large amount of historical electric quantity data of the shared base station, and due to actual engineering factors, some secondary energy consumption equipment is ignored. The next research task is mainly to continuously improve the precision of the model and establish an energy consumption online allocation system, the improvement of the model precision can start from the mechanism analysis, the energy consumption factor is considered as full as possible, and the error is reduced; and establishing a more accurate allocation model, and then obtaining the electric charge allocation proportion on the basis of the model.
The foregoing is illustrative of the present invention and is not to be construed as limiting thereof in any way. Those skilled in the art can make numerous possible variations and modifications to the present invention, or modify equivalent embodiments to equivalent variations, without departing from the scope of the invention, using the teachings disclosed above. Therefore, any simple modification, equivalent change and modification of the present invention and its embodiments according to the technical essence of the present invention shall fall within the protection scope of the technical solution of the present invention.
Claims (10)
1. The method is characterized in that electric equipment independently used by each operator in the shared base station and shared electric equipment are selected to measure electric quantity respectively to obtain direct current electric quantity P1 in the electric equipment independently used by each operator i (i =1,2 \8230;), determining the relation between the power consumption of the electrical equipment independently used by each operator and the power consumption of the electrical equipment shared by the operators, establishing a shared base station electric charge sharing model, and obtaining the sharing amount P2 of the power consumption of the electrical equipment shared by each operator i (i =1,2 \8230;), the sum of P1 and P2 is the share of the power consumption of each operator, where i represents a different operator.
2. The method for accurately allocating electric charge of a large-scale shared base station as claimed in claim 1, wherein the shared base station electric charge allocation model includes environmental impact factors, the environmental impact factors include geographical location impact factors, climate impact factors; a building impact factor; the geographic position influence factors comprise urban area influence factors and rural area influence factors; the climate influence factor comprises a temperature influence factor and a humidity influence factor; building form factor, building area factor and enclosure material factor.
3. The method for accurately allocating electric charges to a mass-shared base station according to claim 1, comprising the steps of:
step S1: selecting part of shared base stations as data acquisition points;
step S2: performing a meter hanging test on the direct current electrical equipment independently used by each operator in the selected shared base station, and recording the current value of the direct current electrical equipment; collecting environmental influence factors of a test base station;
and step S3: establishing a database according to the electric charge detail data of the selected sharing base station and the direct current electric meter data acquired by the meter hanging test; establishing a relation between direct current electric meter data acquired by a meter hanging test, base station environment influence factors and power consumption of operator shared electrical equipment in a shared base station, and establishing a shared base station electric charge sharing model:
y i ＝β 0 +β 1 x i1 +β 2 x i2 +…β p x ip +α 1 u 1 +α 2 u 2 +α 3 u 3 +α 4 u 4 +α 5 u 5 +ε (1)
wherein, y i Indicates the total energy consumption value of the shared electrical equipment, i indicates the sequence number of the shared base station, x i1 ，x i2 ，…，x ip Respectively representing the direct current meter reading, beta, collected by a wall-mounted meter 0 Indicating error compensation, beta 1 、β 2 ，…β p ，α 1 、α 2 、α 3 、α 4 、α 5 Expressing the regression equation coefficients, p different operators, u 1 Representing a geographical influence factor u 1 =1 denotes an urban area influence factor, u 1 =2 denotes rural influence factor, u 2 Denotes the temperature influence factor, u 5 Factor affecting humidity, u 3 Represents a building area influence factor, u 4 Denotes the enclosure material influence factor, u 4 =1 denotes that the enclosure is a brick-concrete structure, u 4 =2 represents that the enclosure is a board house structure, and epsilon represents a random error term;
and step S4: simplifying an electric charge sharing model and optimizing parameters of the sharing model;
carrying out significance test on the regression coefficient, eliminating independent variables which have no obvious influence on the dependent variable or can be replaced by the action of other influence factors, and finally establishing a simpler regression equation, wherein each independent variable in the regression equation is a main factor influencing the change of the dependent variable;
step S5: and determining parameters of the optimal allocation model to obtain the electric charge allocation proportion of the electric equipment shared by each operator, and further obtaining the allocation amount of the energy consumption of the electric equipment shared by each operator.
4. The method for accurately allocating electric charges to a large-scale shared base station according to claim 3, wherein in step S4, the environmental variables of the base station that have little influence on the total energy consumption of the base station are eliminated. Establishing a multiple linear regression fitting model for the total energy consumption value of the shared electrical equipment in the shared base station and the measured value of the direct current electric meter of each operator to optimize and simplify parameters, wherein the fitness function of the parameter optimization is as follows:
wherein Q (theta) is a fitness function value, y i Representing the total energy consumption value of the shared electrical equipment; n represents the number of shared base stations; i represents the sequence number of the shared base station; beta is a 0 Denotes the adjustment factor, p denotes different operators, x i1 ，x i2 ，…，x ip Respectively representing the direct current meter readings collected by the hanging meter; β represents the regression equation coefficient.
5. The method for accurately allocating electric charges to a mass-shared base station according to claim 1,
step Sa: selecting part of shared base stations as data acquisition points;
step Sb: performing a meter-hanging test on the direct-current electrical equipment independently used by each operator in the selected shared base station, and recording the current value of the direct-current electrical equipment;
step Sc: establishing a database according to the electric charge detail data of the selected sharing base station and the direct current electric meter data acquired by the meter hanging test; establishing a relation between direct current electric meter data acquired by a meter hanging test and power consumption of electrical equipment shared by operators in a shared base station, and establishing an electric charge sharing model of the shared base station;
step Sd: optimizing parameters of the apportionment model;
step Se: and determining parameters of the optimal allocation model to obtain the electric charge allocation proportion of the electric equipment shared by each operator, and further obtaining the allocation amount of the energy consumption of the electric equipment shared by each operator.
6. The method for accurately allocating electric charges to a mass-shared base station according to claim 5,
in the step Sc, performing multiple linear regression fitting on the total energy consumption value of the shared electrical equipment in the shared base station and the measured value of the direct current electric meter of each operator to obtain an electric charge sharing model of the shared base station:
wherein Q (theta) is a fitness function value, y i Representing the total energy consumption value of the common electrical equipment; n represents the number of shared base stations; i represents the sequence number of the shared base station; beta is a 0 Denotes the adjustment factor, p denotes different operators, x i1 ，x i2 ，…，x ip Respectively representing the direct current meter readings collected by the hanging meter; β represents the regression equation coefficient.
7. The method for precisely apportioning electric power charges to a large scale shared base station according to claim 4 or 6, wherein β (θ) is the minimum value 0 ，β 1 β 2 …β p The value of (b) is the optimum value.
8. The method of claim 7, wherein the sharing of the power charge among the large scale shared base stations is performed by a common base stationThe sharing proportion of the electrical equipment and the common operators is as follows: beta is a 1 x i1 ：β 2 x i2 ：…：β p x ip 。
9. The method for accurately sharing electric charges of a large-scale shared base station according to claim 8, wherein the number of the operators sharing the common electric equipment in the shared base station is two or three.
10. The method for accurately allocating electric charges to a large-scale shared base station according to claim 1, wherein parameters of the allocation model are optimized using a quantum-behaved particle swarm algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711057721.6A CN107767185A (en) | 2017-11-01 | 2017-11-01 | A kind of accurate methodology of the electricity charge of sharing base |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711057721.6A CN107767185A (en) | 2017-11-01 | 2017-11-01 | A kind of accurate methodology of the electricity charge of sharing base |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107767185A true CN107767185A (en) | 2018-03-06 |
Family
ID=61272001
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711057721.6A Pending CN107767185A (en) | 2017-11-01 | 2017-11-01 | A kind of accurate methodology of the electricity charge of sharing base |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107767185A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110231898A (en) * | 2019-05-10 | 2019-09-13 | 段宏伟 | A kind of intelligent interaction electricity consumption master control switching system based on ubiquitous electric power Internet of Things |
| CN110672918A (en) * | 2019-10-23 | 2020-01-10 | 中国联合网络通信集团有限公司 | Method and device for measuring electric quantity of shared base station |
| CN111722174A (en) * | 2020-05-31 | 2020-09-29 | 宁夏隆基宁光仪表股份有限公司 | System and method for realizing electric energy meter abnormity diagnosis by applying quantum particle group algorithm |
| CN114049017A (en) * | 2021-11-16 | 2022-02-15 | 重庆瑞盾科技发展有限公司 | Electricity charge cost management method and system based on alternating current-direct current intelligent switch ammeter |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102867240A (en) * | 2012-09-11 | 2013-01-09 | 上海交通大学 | Network flow-based fair power transmission allocation data processing system |
| CN202713630U (en) * | 2012-06-16 | 2013-01-30 | 中国电信股份有限公司泰州分公司 | Base station current accurate acquisition transmission unit |
| CN102938111A (en) * | 2012-11-27 | 2013-02-20 | 山东黄金矿业（莱州）有限公司 | Enterprise electric energy consumption apportioning method |
| CN104301985A (en) * | 2014-09-19 | 2015-01-21 | 华北电力大学（保定） | Energy distribution method between power grid and cognition base station in mobile communication |
| CN206178035U (en) * | 2016-10-21 | 2017-05-17 | ***通信集团安徽有限公司芜湖分公司 | Electric quantity gauge system based on sharing basic station |
-
2017
- 2017-11-01 CN CN201711057721.6A patent/CN107767185A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202713630U (en) * | 2012-06-16 | 2013-01-30 | 中国电信股份有限公司泰州分公司 | Base station current accurate acquisition transmission unit |
| CN102867240A (en) * | 2012-09-11 | 2013-01-09 | 上海交通大学 | Network flow-based fair power transmission allocation data processing system |
| CN102938111A (en) * | 2012-11-27 | 2013-02-20 | 山东黄金矿业（莱州）有限公司 | Enterprise electric energy consumption apportioning method |
| CN104301985A (en) * | 2014-09-19 | 2015-01-21 | 华北电力大学（保定） | Energy distribution method between power grid and cognition base station in mobile communication |
| CN206178035U (en) * | 2016-10-21 | 2017-05-17 | ***通信集团安徽有限公司芜湖分公司 | Electric quantity gauge system based on sharing basic station |
Non-Patent Citations (5)
| Title |
|---|
| YINGJIE ZHANG: "The impact of car specifications, prices and incentives for battery electric vehicles in Norway: Choices of heterogeneous consumers", 《TRANSPORTATION RESEARCH PART C: EMERGING TECHNOLOGIES》 * |
| 刘廷亮: "破解基站共享下的电费分摊及节能用电难题", 《电信技术》 * |
| 刘锦萍: "基于改进的粒子群算法的多元线性回归模型参数估计", 《计算机工程与科学》 * |
| 吴云亮: "基于多元非线性回归模型的环型中压配电网最大供电能力评估方法", 《电力自动化设备》 * |
| 汪洋: "基于多元回归算法的电量分析***的设计与实现", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110231898A (en) * | 2019-05-10 | 2019-09-13 | 段宏伟 | A kind of intelligent interaction electricity consumption master control switching system based on ubiquitous electric power Internet of Things |
| CN110672918A (en) * | 2019-10-23 | 2020-01-10 | 中国联合网络通信集团有限公司 | Method and device for measuring electric quantity of shared base station |
| CN110672918B (en) * | 2019-10-23 | 2021-10-15 | 中国联合网络通信集团有限公司 | Method and device for measuring electric quantity of shared base station |
| CN111722174A (en) * | 2020-05-31 | 2020-09-29 | 宁夏隆基宁光仪表股份有限公司 | System and method for realizing electric energy meter abnormity diagnosis by applying quantum particle group algorithm |
| CN114049017A (en) * | 2021-11-16 | 2022-02-15 | 重庆瑞盾科技发展有限公司 | Electricity charge cost management method and system based on alternating current-direct current intelligent switch ammeter |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110471024B (en) | Intelligent electric meter online remote calibration method based on measurement data analysis | |
| CN107767185A (en) | A kind of accurate methodology of the electricity charge of sharing base | |
| CN111398885A (en) | Intelligent electric meter operation error monitoring method combining line loss analysis | |
| CN101789598B (en) | Power system load modelling method | |
| CN104794206B (en) | A kind of substation data QA system and method | |
| CN110070282A (en) | A kind of low-voltage platform area line loss analysis of Influential Factors method based on Synthesis Relational Grade | |
| Nimbargi et al. | Review on AMI technology for Smart Meter | |
| CN109086963B (en) | Line loss theoretical calculation lean management method | |
| CN108448568A (en) | Power distribution network admixture method of estimation based on a variety of time cycle measurement data | |
| CN106535204B (en) | A kind of service coverage method for evaluating quality and device | |
| CN105959975A (en) | Automatic assessment method for energy saving quantity of large-scale base station energy saving project | |
| CN102856896B (en) | On-line analytical method for direct-current transmission loss | |
| CN111969604A (en) | Dynamic calculation method and device for line loss of transformer area based on measured data | |
| CN103106314A (en) | Time sequence probability modeling method for output power of solar photovoltaic power supply | |
| CN103136442A (en) | Method for measuring and proving saved electric energy volume in energy-saving project | |
| CN202907195U (en) | Communication base station energy consumption management system | |
| CN102522742B (en) | Estimation method of external network Thevenin equivalent parameters based on single point measurement information | |
| CN113723719B (en) | Distribution transformation-based near-intermediate load prediction method, device, equipment and medium | |
| CN108490285B (en) | Low-voltage transformer area line loss rate calculation method based on voltage drop method | |
| CN109802408A (en) | Platform area line loss calculation method and device | |
| CN113406555A (en) | Electric quantity metering error monitoring method of alternating current charging station and charging station | |
| CN109829246A (en) | A kind of line parameter circuit value discrimination method based on the suspicious degree of parametric synthesis | |
| Mulenga et al. | The role of aleatory and epistemic uncertainties in a stochastic hosting capacity approach for solar PV | |
| CN109586289A (en) | A kind of power distribution network Multiple Time Scales recurrence dynamic state estimator method and system | |
| CN110752622A (en) | Power distribution network affine state estimation method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |